Speaker
Description
An application of unsupervised machine learning-based anomaly detection to a generic dijet resonance is presented using the full LHC Run 2 dataset collected by ATLAS. A novel variational recurrent neural network (VRNN) is trained over data, specifically large-radius jets that are modeled using a sequence of constituent four-vectors and substructure variables, to identify anomalous jets based on their energy deposition pattern. The VRNN produces a per-jet anomaly score, whose performance is evaluated across a wide variety of hadronic topologies. This score is used to define a model-independent signal region in a search for new particles Y and X in association with a Higgs boson, representing the first application of unsupervised machine learning to an ATLAS analysis. A selection on the anomaly score of the X jet is shown to yield between 5-30% increase in significance across a variety of potential decays, and a comparison of the cross section upper limit on a variety of X hypotheses shows that the anomaly score provides competitive and broad sensitivity compared to traditional high-level variables.
